{"id":"W6964984633","doi":"10.25916/sut.26245457","title":"Finding strong lenses in CFHTLS using convolutional neural networks","year":2017,"lang":"en","type":"article","venue":"Swinburne Research Bank (Swinburne University of Technology)","topic":"Tree-ring climate responses","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Lens (geology); Pattern recognition (psychology); Artificial neural network; Multispectral image; Astronomer; Identification (biology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000487537,0.0005898823,0.0002004809,0.00177892,0.0003783667,0.0007793133,0.0004147617,0.0003830722,0.0008603124],"category_scores_gemma":[0.001638939,0.000220517,0.000430002,0.000997351,0.0003527062,0.0005978768,0.0006385868,0.0002670972,0.0002728153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001131926,"about_ca_system_score_gemma":0.0005582556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05016663,"about_ca_topic_score_gemma":0.0795968,"domain_scores_codex":[0.9996934,0.00003268165,0.00001167112,0.00008026305,0.0001008064,0.00008118578],"domain_scores_gemma":[0.999338,0.0002011482,0.0001760235,0.00008940995,0.0001321062,0.00006330659],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005896416,0.0001839044,0.4539585,0.0002058113,0.0002898552,0.001348302,0.0004430597,0.2043558,0.05808547,0.003200612,0.004240977,0.2730981],"study_design_scores_gemma":[0.00002288563,0.0001283567,0.1560063,0.00003597072,0.0000587665,0.0003023628,0.0002377867,0.8158525,0.02225693,0.002346719,0.002715011,0.00003634673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9637653,0.0003751488,0.03129704,0.0001271206,0.00001061663,0.00003624163,0.0007815022,0.0007448927,0.002862261],"genre_scores_gemma":[0.9789105,0.0001048889,0.01825602,0.00003984678,0.00001116705,0.000008317737,0.001635997,0.00002249053,0.001010809],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05016663,"threshold_uncertainty_score":0.09974927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08975173242342264,"score_gpt":0.3182129115661431,"score_spread":0.2284611791427205,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}